In this article, you will learn how to perform time series forecasting that is used to solve sequence problems.
Time series forecasting refers to the type of problems where we have to predict an outcome based on time dependent inputs. A typical example of time series data is stock market
Object detection is a technology that falls under the broader domain of Computer Vision. It deals with identifying and tracking objects present in images and videos. Object detection has multiple applications such as face detection, vehicle detection, pedestrian counting, self-driving cars, security systems, etc.
The two major objectives of
This is the 20th article in my series of articles on Python for NLP. In the last few articles, we have been exploring deep learning techniques to perform a variety of machine learning tasks, and you should also be familiar with the concept of word embeddings. Word embeddings is a
This is the 19th article in my series of articles on Python for NLP. From the last few articles, we have been exploring fairly advanced NLP concepts based on deep learning techniques. In the last article, we saw how to create a text classification model trained using multiple inputs
This is the 18th article in my series of articles on Python for NLP. In my previous article, I explained how to create a deep learning-based movie sentiment analysis model using Python's Keras library. In that article, we saw how we can perform sentiment analysis of user reviews regarding different
Developing symbols which have some value is a trait unique to humans. Recognizing these symbols and understanding the letters on an image is absolutely normal for us. We never really grasp letters like computers do, we completely base our ability to read them on our sight.
On the other
Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem.
Using transfer learning can dramatically speed up the rate of deployment for an app you are
This is the 17th article in my series of articles on Python for NLP. In the last article, we started our discussion about deep learning for natural language processing.
The previous article was focused primarily towards word embeddings, where we saw how the word embeddings can be used to convert
